{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "215739d3-147d-457d-98d4-83affbe1d6a8",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">带均值柱形图</span>\n",
    "## 一.导入数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6430a32a-7bff-4527-96bd-65a408911ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    店铺名称  内部订单号\n",
      "0   旗舰店1     48\n",
      "1  旗舰店10     48\n",
      "2   旗舰店2     54\n",
      "3   旗舰店3     44\n",
      "4   旗舰店4     57\n",
      "5   旗舰店5     60\n",
      "6   旗舰店6     44\n",
      "7   旗舰店7     44\n",
      "8   旗舰店8     48\n",
      "9   旗舰店9     64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "#导入数据\n",
    "df = pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "shop_names = [f'旗舰店{i}' for i in range(1, 11)]\n",
    "filtered_df = df[df['店铺名称'].isin(shop_names)]\n",
    "shop_order_count = filtered_df.groupby('店铺名称')['内部订单号'].count().reset_index()\n",
    "print(shop_order_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6db612-e7fd-4c38-bdc2-a09b49a0b510",
   "metadata": {},
   "source": [
    "## 二.给<span style=\"color: orange;\">旗舰店——订单数渐变柱形图</span>加上均值线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6ec6220-6bf9-418b-8c48-29a8418e0b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. 读取并处理数据\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "shop_names = [f'旗舰店{i}' for i in range(1, 11)]\n",
    "filtered_df = df[df['店铺名称'].isin(shop_names)]\n",
    "# 统计每个旗舰店的订单数量（按店铺分组，计数内部订单号）\n",
    "shop_order_count = filtered_df.groupby('店铺名称')['内部订单号'].count().reset_index()\n",
    "# 按旗舰店1到10排序\n",
    "shop_order_count['sort_key'] = shop_order_count['店铺名称'].str.extract(r'旗舰店(\\d+)').astype(int)\n",
    "shop_order_count = shop_order_count.sort_values('sort_key').drop('sort_key', axis=1)\n",
    "\n",
    "# 2. 配置绘图参数\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 解决中文显示问题\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 3. 创建蓝色渐变色映射\n",
    "colors = [(0.7, 0.8, 1), (0.2, 0.4, 0.8)]  # 浅蓝到深蓝\n",
    "cmap = LinearSegmentedColormap.from_list('blue_gradient', colors)\n",
    "\n",
    "# 计算均值\n",
    "mean_value = shop_order_count['内部订单号'].mean()\n",
    "\n",
    "# 4. 绘制图表\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "fig.patch.set_facecolor('#1E293B')  # 设置深蓝色背景\n",
    "ax.set_facecolor('#1E293B')\n",
    "\n",
    "# 绘制渐变柱状图\n",
    "bars = ax.bar(\n",
    "    shop_order_count['店铺名称'],\n",
    "    shop_order_count['内部订单号'],\n",
    "    color=cmap(np.linspace(0, 1, len(shop_order_count))),\n",
    "    width=0.6\n",
    ")\n",
    "\n",
    "# 在柱子上方添加数值标签\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    ax.text(\n",
    "        bar.get_x() + bar.get_width() / 2.,\n",
    "        height + 1,\n",
    "        f'{int(height)}',\n",
    "        ha='center', va='bottom', color='white'\n",
    "    )\n",
    "\n",
    "# 5. 设置标题、说明与轴标签\n",
    "ax.set_title('各旗舰店订单数量分布', fontsize=16, color='white', pad=20)\n",
    "# 订单最多的旗舰店及占比\n",
    "max_shop = shop_order_count.loc[shop_order_count['内部订单号'].idxmax(), '店铺名称']\n",
    "max_count = shop_order_count['内部订单号'].max()\n",
    "total_count = shop_order_count['内部订单号'].sum()\n",
    "max_ratio = (max_count / total_count) * 100\n",
    "ax.text(0.5, 0.9, f'{max_shop}订单最多占比总订单的{max_ratio:.1f}%', \n",
    "        transform=ax.transAxes, ha='center', color='white', fontsize=12)\n",
    "\n",
    "ax.set_xlabel('店铺名称', fontsize=12, color='white', labelpad=10)\n",
    "ax.set_ylabel('订单数量', fontsize=12, color='white', labelpad=10)\n",
    "\n",
    "# 6. 调整坐标轴样式\n",
    "ax.tick_params(axis='x', colors='white', rotation=0)\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "\n",
    "# 设置纵轴范围，预留顶部空间显示标签\n",
    "ax.set_ylim(0, shop_order_count['内部订单号'].max() * 1.2)\n",
    "\n",
    "# 隐藏顶部和右侧边框\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "# 设置剩余边框颜色为白色\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "# 绘制均值线\n",
    "ax.axhline(y=mean_value, color='yellow', linestyle='-', label=f'平均值: {mean_value:.0f}')\n",
    "ax.text(ax.get_xlim()[1], mean_value, f'平均值: {mean_value:.0f}', va='center', ha='left', color='yellow')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e927b4-2d76-4966-af09-0d4eac30f560",
   "metadata": {},
   "source": [
    "## 分析：\n",
    "#### 带均值柱形图加入一条表示平均值的水平线，可以清晰分看出哪些店铺的订单量超出平均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "929f468e-b899-42cf-8d66-b7c6ae459f1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
